binary logistic regression
Binary logistic regression modeling with TensorFlow™
... Introduction Binary logistic regression modeling is probably one of the most commonly used approaches for predictive analytics in clinical ...
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Application of binary logistic regression in credit scoring
... 37 Conclusion In this Master’s thesis we used real data collected from 3985 loan applicants both “good” and “bad”. Our dependent variable took 2 values: “0” or “1” depending on whether the applicant was bad or good ...
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Validation and Performance Analysis of Binary Logistic Regression Model
... predictive logistic regression model have been introduced in the ...fitted logistic regression model has significant predictive ability for future ...fitted binary logistic ...
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factors for temporomandibular disorder: Binary logistic regression analysis.
... Multivariate analysis was performed using the binary logistic regression model to determine the OR for patients with and without TMD. Gender, age, economic class and marital status were incorporated ...
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The Application of Binary Logistic Regression Analysis on Staff Performance Appraisal
... Abstract: This study investigates and models salient factors that influences the performance of staff in an appraisal exercise and as well estimate the odds of these factors influencing the outcome variable (performance ...
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Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight
... The binary logistic regression analysis was performed to determine the effects of gender, neck circumference, waist to height ratio, and physical activity index on the likelihood that subjects have ...
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Binary logistic regression analysis on addmiting students using jamb score
... ‘Binary logistic regression analysis on admitting students using jamb score’’ A legitimate research hypothesis posed to the data was that “the likelihood that the prospective candidate seeking for ...
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Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm
... binary logistic regression classification algorithm using accuracy measurement technique, AUC, f-measure and g-mean. 2.2 RWO-Sampling Method The RWO-Sampling method includes an oversampling method ...
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COVARIATES AND SAMPLE SIZE EFFECTS ON PARAMETER ESTIMATION FOR BINARY LOGISTIC REGRESSION MODEL
... This study examined the effect of three types of covariate (continuous, count, categorical) on the parameter estimation in binary logistic regression model. The results of this simulation study show ...
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Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler
... Logistic regression has been extensively used to perform classifica- tion in machine learning and signal/image ...Bayesian binary logistic regression problem by relying on the recent ...
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Prediction of Antibiotics Residues in Raw Milk by Using Binary Logistic Regression Model
... , binary logistic regression model to predict the presence or absence of antibiotic residues in milk were determined using the software SPSS Statistics ...
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Analysis on Predicting Spousal Physical Violence in the Philippines Using Binary Logistic Regression
... Abstract: This study was conducted for the purpose of identifying the factors that contribute to spousal physical violence in the Philippines using binary logistic regression. The variables are the ...
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Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler
... Logistic regression has been extensively used to perform classifica- tion in machine learning and signal/image ...Bayesian binary logistic regression problem by relying on the recent ...
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Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status
... with binary outcome ...and binary logistic regression were applied and compared in their ability to classifying disease-free subjects and those with diabetes mellitus(DM) diagnosed by glucose ...
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On the Comparison of Deep Learning Neural Network and Binary Logistic Regression for Classifying the Acceptance Status of
... and Binary Logistic Regression are modern and classical data mining analysis tools that can be used to classify data on Bidikmisi scholarship acceptance in East Java Province, ...and Binary ...
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Machine Learning: An Approach in Identifying Risk Factors for Coercion Compared to Binary Logistic Regression
... coercion binary logistic regression analysis was used with coercion/no coercion as the dependent ...the binary logistic regression model was assessed by the receiver operating ...
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Applying Penalized Binary Logistic Regression with Correlation Based Elastic Net for Variables Selection
... Conclusion A study of a new penalization method based on CBEP was done by application to binary logistic regression. Three penalization methods in addition to CBEP, including elastic net, LASSO, and ...
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A Multivariate Binary Logistic Regression Modeling For Assessing Various Risk Factors That Affect Diabetes
... of India. However, due to prominent screening processes and anti-diabetic therapy, the Indian urban has the advantage of such health benefits in comparison to rural residents. So, quantifying the prevalence of diabetes ...
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No rationale for 1 variable per 10 events criterion for binary logistic regression analysis
... regression analysis is weak. So far, much of this evidence comes from minimal EPV simulation studies that stud- ied the performance of estimating the relations between covariates and outcome. Our simulation study ...
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Binary Logistic Regression
... Stress does not contribute to the model -- though it contributed to the main effect model The Gender * Loneliness interaction does not contribute to the model. The Gender * Stress inter[r] ...
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